SoroushVahidi/scidocs huggingface dataset:
Gait planning and behavior-based control for statically-stable walking robots
(1995)

A substantial portion of the Earth is inaccessible to any sort of wheeled mechanism— natural obstacles like large rocks, loose soil, deep ravines, and steep slopes conspire to render rolling locomotion ineffective. Hills, mountains, shores, seabeds, as well as the moon and other planets present similar terrain challenges. In many of these natural terrains, legs are well-suited. They can avoid small obstacles by making discrete contacts and passing up undesirable footholds. Legged mechanisms can climb over obstacles and step across ditches, surmounting terrain discontinuities of body-scale while staying level and stable. To achieve their potential, legged robots must coordinate their leg motions to climb over, step across and walk in natural terrain. These coordinated motions, which support and propel the robot, are called a gait. This thesis develops a new method of gait planning and control that enables statically-stable walking robots to produce a gait that is robust and productive in natural terrain. Independent task-achieving processes, called gait behaviors, establish a nominal gait, adapt it to the terrain, and react to disturbances like bumps and slips. Gait controlled in this way enabled the robot Dante II to walk autonomously in natural terrain, including the volcanic crater of Mount Spurr. This method extends to other walking robots as demonstrated by a generalized hexapod that performs the variety of gaits seen in six-legged insects, as well as aperiodic free gaits. The ability to change gait patterns on-the-fly with continuous, stable motion is a new development that enables robots to behave more like animals in adapting their gait to terrain. Finally, this thesis describes why walking robots need predictive plans as well as reflexive behaviors to walk effectively in the real world. It presents a method of guiding the behavior of a walking robot by planning distinct attributes of the desired gait. This partitioning of gait planning avoids the complexity of high degree-of-freedom motion planning. The ability to plan and foresee changes in gait improves performance while maintaining robust safety and stability. Abstract ii iii Acknowledgment
Developing a Taxonomy of Dark Triad Triggers at Work – A Grounded Theory Study Protocol
(2017)

In past years, research and corporate scandals have evidenced the destructive effects of the dark triad at work, consisting of narcissism (extreme self-centeredness), psychopathy (lack of empathy and remorse) and Machiavellianism (a sense of duplicity and manipulativeness). The dark triad dimensions have typically been conceptualized as stable personality traits, ignoring the accumulating evidence that momentary personality expressions - personality states - may change due to the characteristics of the situation. The present research protocol describes a qualitative study that aims to identify triggers of dark triad states at work by following a grounded theory approach using semi-structured interviews. By building a comprehensive categorization of dark triad triggers at work scholars may study these triggers in a parsimonious and structured way and organizations may derive more effective interventions to buffer or prevent the detrimental effects of dark personality at work.
Web Engineering: A New Discipline for Development of Web-Based Systems
(2001)

In most cases, development of Web-based systems has been ad hoc, lacking systematic approach and quality control and assurance procedures. Hence, there is now legitimate and growing concern about the manner in which Web-based systems are developed and their long-term quality and integrity. Web Engineering, an emerging new discipline, advocates a process and a systematic approach to development of high quality Web-based systems. It promotes the establishment and use of sound scientific, engineering and management principles, and disciplined and systematic approaches to development, deployment and maintenance of Web-based systems. This paper gives an introductory overview on Web Engineering. It presents the principles and roles of Web Engineering, assesses the similarities and differences between development of traditional software and Web-based systems, identifies key Web engineering activities and reviews some of the ongoing work in this area. It also highlights the prospects of Web engineering and the areas that need further study.
Negotiation and Cooperation in Multi-Agent Environments
(1997)

Automated intelligent agents inhabiting a shared environment must coordinate their activities Cooperation not merely coordination may improve the performance of the individual agents or the overall behavior of the system they form Research in Distributed Arti cial Intelligence DAI addresses the problem of designing automated intelligent systems which interact e ectively DAI is not the only eld to take on the challenge of understanding cooperation and coordination There are a variety of other multi entity environments in which the entities coordinate their activity and cooperate Among them are groups of people animals particles and computers We argue that in order to address the challenge of building coordinated and collaborated intelligent agents it is bene cial to combine AI techniques with methods and techniques from a range of multi entity elds such as game theory operations research physics and philosophy To support this claim we describe some of our projects where we have successfully taken an interdisciplinary approach We demonstrate the bene ts in applying multi entity methodologies and show the adaptations modi cations and extensions necessary for solving the DAI problems This is an extended version of a lecture presented upon receipt of the Computers and Thought Award at the th International Joint Conference on Arti cial Intelligence in Montreal Canada August
Wikipedia Chemical Structure Explorer: substructure and similarity searching of molecules from Wikipedia
(2015)

BACKGROUND
Wikipedia, the world's largest and most popular encyclopedia is an indispensable source of chemistry information. It contains among others also entries for over 15,000 chemicals including metabolites, drugs, agrochemicals and industrial chemicals. To provide an easy access to this wealth of information we decided to develop a substructure and similarity search tool for chemical structures referenced in Wikipedia.


RESULTS
We extracted chemical structures from entries in Wikipedia and implemented a web system allowing structure and similarity searching on these data. The whole search as well as visualization system is written in JavaScript and therefore can run locally within a web page and does not require a central server. The Wikipedia Chemical Structure Explorer is accessible on-line at www.cheminfo.org/wikipedia and is available also as an open source project from GitHub for local installation.


CONCLUSIONS
The web-based Wikipedia Chemical Structure Explorer provides a useful resource for research as well as for chemical education enabling both researchers and students easy and user friendly chemistry searching and identification of relevant information in Wikipedia. The tool can also help to improve quality of chemical entries in Wikipedia by providing potential contributors regularly updated list of entries with problematic structures. And last but not least this search system is a nice example of how the modern web technology can be applied in the field of cheminformatics. Graphical abstractWikipedia Chemical Structure Explorer allows substructure and similarity searches on molecules referenced in Wikipedia.
Word Embeddings and Their Use In Sentence Classification Tasks
(2016)

This paper has two parts. In the first part we discuss word embeddings. We discuss the need for them, some of the methods to create them, and some of their interesting properties. We also compare them to image embeddings and see how word embedding and image embedding can be combined to perform different tasks. In the second part we implement a convolutional neural network trained on top of pre-trained word vectors. The network is used for several sentence-level classification tasks, and achieves state-of-art (or comparable) results, demonstrating the great power of pre-trainted word embeddings over random ones.
A critical review of blockchain and its current applications
(2017)

Blockchain technology has been known as a digital currency platform since the emergence of Bitcoin, the first and the largest of the cryptocurrencies. Hitherto, it is used for the decentralization of markets more generally, not exclusively for the decentralization of money and payments. The decentralized transaction ledger of blockchain could be employed to register, confirm, and send all kinds of contracts to other parties in the network. In this paper, we thoroughly review state-of-the-art blockchain-related applications emerged in the literature. A number of published works were carefully included based on their contributions to the blockchain's body of knowledge. Several remarks are explored and discussed in the last section of the paper.
Hidden Voice Commands
(2016)

Voice interfaces are becoming more ubiquitous and are now the primary input method for many devices. We explore in this paper how they can be attacked with hidden voice commands that are unintelligible to human listeners but which are interpreted as commands by devices. We evaluate these attacks under two different threat models. In the black-box model, an attacker uses the speech recognition system as an opaque oracle. We show that the adversary can produce difficult to understand commands that are effective against existing systems in the black-box model. Under the white-box model, the attacker has full knowledge of the internals of the speech recognition system and uses it to create attack commands that we demonstrate through user testing are not understandable by humans. We then evaluate several defenses, including notifying the user when a voice command is accepted; a verbal challenge-response protocol; and a machine learning approach that can detect our attacks with 99.8% accuracy.
Supply Chain Sourcing Under Asymmetric Information
(2010)

We study a supply chain with two suppliers competing over a contract to supply components to a manufacturer. One of the suppliers is a big company for whom the manufacturer’s business constitutes a small part of his business. The other supplier is a small company for whom the manufacturer’s business constitutes a large portion of his business. We analyze the problem from the perspective of the big supplier and address the following questions: What is the optimal contracting strategy that the big supplier should follow? How does the information about the small supplier’s production cost affect the profits and contracting decision? How does the existence of the small supplier affect profits? By studying various information scenarios regarding the small supplier’s and the manufacturer’s production cost, we show, for example, that the big supplier benefits when the small supplier keeps its production cost private. We quantify the value of information for the big supplier and the manufacturer. We also quantify the cost (value) of the alternative-sourcing option for the big supplier (the manufacturer). We determine when an alternative-sourcing option has more impact on profits than information. We conclude with extensions and numerical examples to shed light on how system parameters affect this supply chain.
A Dependency Graph Approach for Fault Detection and Localization Towards Secure Smart Grid
(2011)

Fault diagnosis in power grids is known to be challenging, due to the massive scale and spatial coupling therein. In this study, we explore multiscale network inference for fault detection and localization. Specifically, we model the phasor angles across the buses as a Markov random field (MRF), where the conditional correlation coefficients of the MRF are quantified in terms of the physical parameters of power systems. Based on the MRF model, we then study decentralized network inference for fault diagnosis, through change detection and localization in the conditional correlation matrix of the MRF. Particularly, based on the hierarchical topology of practical power systems, we devise a multiscale network inference algorithm that carries out fault detection and localization in a decentralized manner. Simulation results are used to demonstrate the effectiveness of the proposed approach.
Secure and reconfigurable network design for critical information dissemination in the Internet of battlefield things (IoBT)
(2017)

The Internet of things (IoT) is revolutionizing the management and control of automated systems leading to a paradigm shift in areas such as smart homes, smart cities, health care, transportation, etc. The IoT technology is also envisioned to play an important role in improving the effectiveness of military operations in battlefields. The interconnection of combat equipment and other battlefield resources for coordinated automated decisions is referred to as the Internet of battlefield things (IoBT). IoBT networks are significantly different from traditional IoT networks due to the battlefield specific challenges such as the absence of communication infrastructure, and the susceptibility of devices to cyber and physical attacks. The combat efficiency and coordinated decision-making in war scenarios depends highly on real-time data collection, which in turn relies on the connectivity of the network and the information dissemination in the presence of adversaries. This work aims to build the theoretical foundations of designing secure and reconfigurable IoBT networks. Leveraging the theories of stochastic geometry and mathematical epidemiology, we develop an integrated framework to study the communication of mission-critical data among different types of network devices and consequently design the network in a cost effective manner.
Multi-task learning of dish detection and calorie estimation
(2018)

In recent years, a rise in healthy eating has led to various food management applications, which have image recognition to automatically record meals. However, most image recognition functions in existing applications are not directly useful for multiple-dish food photos and cannot automatically estimate food calories. Meanwhile, methodologies on image recognition have advanced greatly because of the advent of Convolutional Neural Network, which has improved accuracies of various kinds of image recognition tasks, such as classification and object detection. Therefore, we propose CNN-based food calorie estimation for multiple-dish food photos. Our method estimates food calories while simultaneously detecting dishes by multi-task learning of food calorie estimation and food dish detection with a single CNN. It is expected to achieve high speed and save memory by simultaneous estimation in a single network. Currently, there is no dataset of multiple-dish food photos annotated with both bounding boxes and food calories, so in this work, we use two types of datasets alternately for training a single CNN. For the two types of datasets, we use multiple-dish food photos with bounding-boxes attached and single-dish food photos with food calories. Our results show that our multi-task method achieved higher speed and a smaller network size than a sequential model of food detection and food calorie estimation.
Chernoff-Hoeffding Inequality and Applications
(2012)

When dealing with modern big data sets, a very common theme is reducing the set through a random process. These generally work by making “many simple estimates” of the full data set, and then judging them as a whole. Perhaps magically, these “many simple estimates” can provide a very accurate and small representation of the large data set. The key tool in showing how many of these simple estimates are needed for a fixed accuracy trade-off is the Chernoff-Hoeffding inequality [2, 6]. This document provides a simple form of this bound, and two examples of its use.
The Unbearable Automaticity of Being
(1999)

What was noted by E. J. hanger (1978) remains true today: that much of contemporary psychological research is based on the assumption that people are consciously and systematically processing incoming information in order to construe and interpret their world and to plan and engage in courses of action. As did E. J. hanger, the authors question this assumption. First, they review evidence that the ability to exercise such conscious, intentional control is actually quite limited, so that most of moment-to-moment psychological life must occur through nonconscious means if it is to occur at all. The authors then describe the different possible mechanisms that produce automatic, environmental control over these various phenomena and review evidence establishing both the existence of these mechanisms as well as their consequences for judgments, emotions, and behavior. Three major forms of automatic self-regulation are identified: an automatic effect of perception on action, automatic goal pursuit, and a continual automatic evaluation of one's experience. From the accumulating evidence, the authors conclude that these various nonconscious mental systems perform the lion's share of the self-regulatory burden, beneficently keeping the individual grounded in his or her current environment.
Intrinsically Motivated Learning of Hierarchical Collections of Skills
(2004)

Humans and other animals often engage in activities for their own sakes rather than as steps toward solving practical problems. Psychologists call these intrinsically motivated behaviors. What we learn during intrinsically motivated behavior is essential for our development as competent autonomous entities able to efficiently solve a wide range of practical problems as they arise. In this paper we present initial results from a computational study of intrinsically motivated learning aimed at allowing artificial agents to construct and extend hierarchies of reusable skills that are needed for competent autonomy. At the core of the model are recent theoretical and algorithmic advances in computational reinforcement learning, specifically, new concepts related to skills and new learning algorithms for learning with skill hierarchies.
An examination of the determinants of customer loyalty in mobile commerce contexts
(2006)

While the importance of customer loyalty has been recognized in marketing literature for at least three decades, the development and empirical validation of a customer loyalty model in a mobile commerce (m-commerce) context had not been addressed. The purpose of our study was to develop and validate such a customer loyalty model. Based on IS and marketing literature, a comprehensive set of constructs and hypotheses were compiled with a methodology for testing them. A questionnaire was constructed and data were collected from 255 users of m-commerce systems in Taiwan. Structural modeling techniques were then applied to analyze the data. The results indicated that customer loyalty was affected by perceived value, trust, habit, and customer satisfaction, with customer satisfaction playing a crucial intervening role in the relationship of perceived value and trust to loyalty. Based on the findings, its implications and limitations are discussed. # 2005 Elsevier B.V. All rights reserved.
Principles and practice in reporting structural equation analyses.
(2002)

Principles for reporting analyses using structural equation modeling are reviewed, with the goal of supplying readers with complete and accurate information. It is recommended that every report give a detailed justification of the model used, along with plausible alternatives and an account of identifiability. Nonnormality and missing data problems should also be addressed. A complete set of parameters and their standard errors is desirable, and it will often be convenient to supply the correlation matrix and discrepancies, as well as goodness-of-fit indices, so that readers can exercise independent critical judgment. A survey of fairly representative studies compares recent practice with the principles of reporting recommended here.
154-2008: Understanding Your Customer: Segmentation Techniques for Gaining Customer Insight and Predicting Risk in the Telecom Industry
(2008)

The explosion of customer data in the last twenty years has increased the need for data mining aimed at customer relationship management (CRM) and understanding the customer. It is well known that the telecom sector consists of customers with a wide array of customer behaviors. These customers pose different risks, making it imperative to implement different treatment strategies to maximize shareholder profit and improve revenue generation. Segmentation is the process of developing meaningful customer groups that are similar based on individual account characteristics and behaviors. The goal of segmentation is to know your customer better and to apply that knowledge to increase profitability, reduce operational cost, and enhance customer service. Segmentation can provide a multidimensional view of the customer for better treatment targeting. An improved understanding of customer risk and behaviors enables more effective portfolio management and the proactive application of targeted treatments to lift account profitability. In this paper we outline several segmentation techniques using SAS Enterprise MinerTM. INTRODUCTION Rapid advances in computer technology and an explosion of data collection systems over the last thirty years make it more critical for business to understand their customers. Companies employing data driven analytical strategies often enjoy a competitive advantage. Many organizations across several industries widely employ analytical models to gain a better understanding of their customers. They use these models to predict a wide array of events such as behavioral risk, fraud, or the likelihood of response. Regardless of the predictive variable, a single model may not perform optimally across the target population because there may be distinct segments with different characteristics inherent in the population. Segmentation may be done judgmentally based on experience, but such segmentation schema is limited to the use of only a few variables at best. True multivariate segmentation with the goal of identifying the different segments in your population is best achieved through the use of cluster analysis. Clustering and profiling of the customer base can answer the following questions: ♦ Who are my customers? ♦ How profitable are my customers? ♦ Who are my least profitable customers? ♦ Why are my customers leaving? ♦ What do my best customers look like? This paper discusses the use of SAS Enterprise Miner to segment a population of customers using cluster analysis and decision trees. Focus is placed on the methodology rather than the results to ensure the integrity and confidentiality of customer data. Other statistical strategies are presented in this paper which could be employed in the pursuit of further customer intelligence. It has been said that statistical cluster analysis is as much art as it is science because it is performed without the benefit of well established statistical criteria. Data Mining and Predictive Modeling SAS Global Forum 2008